class_docstr: Building Block of RepLKNet
  
  This class defines overparameterized large kernel conv block
  introduced in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
  
  Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
docstr: Helper method to construct conv-batchnorm layers.
  
  Args:
      kernel_size: Size of the convolution kernel.
      padding: Zero-padding size.
  
  Returns:
      A nn.Sequential Conv-BN module.
docstr_tok: ["Helper","method","to","construct","conv-batchnorm","layers",".","Args",":","kernel_size",":","Size","of","the","convolution","kernel",".","padding",":","Zero-padding","size",".","Returns",":","A","nn.Sequential","Conv-BN","module","."]
code: def _conv_bn(
      self, kernel_size: int, padding: int = 0
  ) -> nn.Sequential:
      """Helper method to construct conv-batchnorm layers.
  
      Args:
          kernel_size: Size of the convolution kernel.
          padding: Zero-padding size.
  
      Returns:
          A nn.Sequential Conv-BN module.
      """
      mod_list = nn.Sequential()
      mod_list.add_module(
          "conv",
          nn.Conv2d(
              in_channels=self.in_channels,
              out_channels=self.out_channels,
              kernel_size=kernel_size,
              stride=self.stride,
              padding=padding,
              groups=self.groups,
              bias=False,
          ),
      )
      mod_list.add_module(
          "bn", nn.BatchNorm2d(num_features=self.out_channels)
      )
      return mod_list
  
file_path: ml-fastvit/models/modules/replknet.py
imports: from typing import Tuple
  import torch
  import torch.nn
label_desc: Use image node_id 6 for calling the ReparamLargeKernelConv obj's underlying member method code with example usage: obj._conv_bn(kernel_size, padding) and returns: mod_list